Face hallucination via Cauchy regularized sparse representation

In dictionary-learning-based face hallucination, the testing image is represented as a linear combination of the training samples, and how to obtain the optimal coefficients is the primary issue. Sparse representation (SR) has ever been widely used in face hallucination, however, due to the fact that SR overemphasizes the sparsity, the obtained linear combination coefficients turn out far aggressively sparse, then leading to unsatisfactory hallucinated results. In this paper, we present a moderately sparse prior model for face hallucination problem with the L1 norm penalty in classic SR replaced by a Cauchy penalty term. An iterative optimization is further presented to solve the minimization of Cauchy regularized objective function. The experimental results on public face database demonstrate that our method is much more effective than state-of-the-art methods.